Finns det någon substans i uttrycket ”du blir smartare av att äta fisk”, eller är det bara en myt? Syftet med studien är att undersöka om kosten, sömnen och fysisk aktivitet har någon betydelse för elevernas studieresultat, i form av betyg i grundskolans årskurs 8, samt om det finns skillnader kopplat till kön. Att belysa vad som är viktigt i kostens sammansättning, måltidsvanor och hur det påverkar kognitionen. Andra viktiga faktorer är fysisk aktivitet och sömn, för att uppnå goda studieresultat. Enkätmetod, tabeller, principalkomponentanalys och Pearssons korrelationskoefficient har använts. Tendensen är att ju bättre hälsovanor desto bättre betyg. Förhoppningsvis kommer denna studie att bidra till att vidga kunskaperna kring sambandet mellan goda hälsovanor (med ingående komponenter kost, sömn och fysisk aktivitet) respektive ohälsosamma hälsovanor och studieresultat inom Hem- och konsumentkunskap. Vänligen skriv ut arbetet i färg, det ökar förståelsen. / Is there any substance in the phrase "you get smarter of eating fish" or is it just a myth? The purpose of the study is to investigate whether diet, sleep and physical activity have any significance for students' study results, in terms of grades in compulsory school grade eight, and if there are differences related to gender. To highlight what is important in the composition of the diet, eating habits and how it affects cognition. Other important factors are physical activity and sleep, to achieve good study results. Survey methods, tables, principal component analysis and Pearsson correlation coefficient have been used. The tendency is that the better the health habits, the better the grade. Hopefully, this study will help to broaden the knowledge of the link between healthy lifestyle (including components of diet, sleep and physical activity) and unhealthy health habits and study results in Home- and consumer knowledge.Please print the work in color, it increases understanding.

The study aimed at evaluating tree and shrub vegetation concerning the slope of the soil in Seasonal Deciduous Forest. The vegetation assessment was conducted in 14 plots systematically distributed in the forest and divided into subplots of 10 x 10 m, in which individuals with circumference 1.3 m of soil (CAP) ≥ 30 cm were observed. These individuals represented class I while individuals with a CAP ≥ 15 < 30 cm represented class II. Vegetation was analyzed by means of the TWINSPAN (Two-way Indicator Species Analysis) method to classify groups, within which the horizontal structure of the forest was studied. In 36 subplots, morphological description of the soil profile was carried out, samples were collected for chemical analysis and clay was determined at three depths (0-10, 10-20 and 20-30 cm), as well as slope, which was classified as low slope (1 ≥ 15), medium slope (15 ≥ 35) and high slope (≥35). The relationship between clusters of vegetation, slope and soil characteristics was studied by means of Principal Component Analysis (PCA). In G1, indicator species were Trichilia claussenii, Cupania vernalis and Crysophyllum marginatum and, in G2, Luehea divaricata and Sebastiania commersoniana. In terms of horizontal structure, these species were among the three best represented ones. Both clusters occurred in areas of variable slope, however, G2 took place in an environment which was more susceptible to interference in the vegetation. From PCA, it could be observed that soil characteristics and slope explained 26% of the total variability, and, out of these, 72% was explained in the main component 1, showing a strong correlation between soil characteristics and the presence of phytosociological groups in different slopes. This relationship is stronger for the presence of the two groups in the ranges of medium and high slope, on the other hand, in terms of low slope, the two groups showed no positive correlation with soil characteristics. For G1 in high slope and G2 in medium and high slope, correlation is positive concerning the whole characteristics of cationic exchange. However, G1 in medium slope was strongly correlated to the complex of exchangeable acidity. G2 at higher slopes also shows high correlation with clay, the finer texture may confer a greater degree of aggregation and reactivity, allowing the development of large species such as Luehea divaricata and Cordia americana. In general, due to restrictions of soil and slope, maintaining of the remnants is prior, aiming at important environmental services for the region. / O estudo objetivou avaliar a vegetação arbórea e arbustiva em relação à formação de agrupamentos, declividade do terreno e influência do solo nos agrupamentos em Floresta Estacional Decidual. A avaliação da vegetação foi realizada em 14 parcelas distribuídas sistematicamente na floresta e divididas em subparcelas de 10 x 10 m, onde foram observados os indivíduos com CAP (circunferência a 1,3 m do solo) ≥ 30 cm, os quais representaram a classe I, e indivíduos com 15 ≤ CAP < 30 cm, representando a classe II. Na análise da vegetação utilizou-se o método TWINSPAN (Two-way Indicator Species Analysis) para classificação de grupos, dentro dos quais foi estudada a estrutura horizontal da floresta. Em 36 subparcelas foi realizada a descrição morfológica do perfil do solo, coletada amostra para análise química e determinação de argila em três profundidades (0-10, 10-20 e 20-30 cm), além da declividade, que foi classificada em baixa (1 ≥ 15º), média (15 ≥ 35º) e alta (≥ 35º). A relação com os grupos e características do solo foi estudada por meio de Análise de Componente Principal (PCA). No G1 as espécies indicadoras foram Trichilia claussenii, Cupania vernalis e Crysophyllum marginatum e no grupo G2 Luehea divaricata e Sebastiania commersoniania. Na estrutura horizontal essas espécies estiveram entre as três melhores representadas. Ambos os agrupamentos ocorreram em áreas com variáveis declividades, entretanto, o G2 ocorreu em ambiente mais susceptível às interferências na vegetação. A partir do PCA observou-se que as características do solo e declividade explicaram 26% da variabilidade total, sendo que desses 72% foi explicado no componente principal 1, demonstrando forte correlação entre as características dos solos e a presença dos grupos fitossociológicos em diferentes declividades. Essa relação foi mais expressiva para a presença dos dois grupos nos intervalos de média e alta declividade, por outro lado, em declividade baixa os dois grupos não apresentaram correlação positiva com as características do solo. Para G1 em alta e G2 em média e alta declividade, a correlação é positiva em relação ao conjunto de características de troca catiônica. Entretanto, o G1 em declividade média teve forte correlação com o complexo de acidez trocável. O G2 em maior declividade também apresenta alta correlação com a argila, essa textura mais fina talvez confira um maior poder de agregação e reatividade, permitindo o desenvolvimento de espécies de grande porte como Luehea divaricata e Cordia americana. De forma geral, devido às restrições de solo e declividade, a manutenção do remanescente é prioritária visando importantes serviços ambientais à região.

To manufacture safe, effective and affordable medicines with greater efficiency, process analytical technology (PAT) has been introduced by the Food and Drug Agency to encourage the pharmaceutical industry to develop and design well-understood processes. PAT requires chemical imaging techniques to be used to collect process variables for real-time process analysis. Multivariate statistical analysis tools and process control tools are important for implementing PAT in the development and manufacture of pharmaceuticals as they enable information to be extracted from the PAT measurements. Multivariate statistical analysis methods such as principal component analysis (PCA) and independent component analysis (ICA) are applied in this thesis to extract information regarding a pharmaceutical tablet. ICA was found to outperform PCA and was able to identify the presence of five different materials and their spatial distribution around the tablet.Another important area for PAT is in improving the control of processes. In the pharmaceutical industry, many of the processes operate in a batch strategy, which introduces difficult control challenges. Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that has been used extensively to extract chemical and physical information from a product sample based on the scattering effect of light. In this thesis, NIR measurements were incorporated as feedback information into several control strategies. Although these controllers performed reasonably well, they could only regulate the NIR spectrum at a number of wavenumbers, rather than over the full spectrum.In an attempt to regulate the entire NIR spectrum, a novel control algorithm was developed. This controller was found to be superior to the only comparable controller and able to regulate the NIR similarly. The benefits of the proposed controller were demonstrated using a benchmark simulation of a batch reactor.

The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by the industry sector to which the individual stocks belong. Joining together PCA with classical linear regression creates principal components regression which is further in the thesis applied to the German DAX 30 index logarithmic returns forecasting using various macroeconomic and financial predictors. The correlation between two energy stocks returns - Chevron and ExxonMobil is forecasted using orthogonal (or PCA) GARCH. The constructed forecast is then compared with the predictions constructed by the conventional multivariate volatility models - EWMA and DCC GARCH.

The healthy condition of a rotating machine leads to safe and cheap operation of almost all industrial facilities and mechanical systems. To achieve such a goal, vibration-based condition monitoring has proved to be a well-accepted technique that detects incipient fault symptoms. The conventional way of On-Bearing Vibration Measurement (OBVM) captures symptoms of different faults, however, it requires a relatively expensive setup, an additional space for the auxiliary devices and cabling in addition to an experienced analyst. On-Shaft Vibration Measurement (OSVM) is an emerging method proposed to offer more reliable Faults Diagnosis (FD) tools with less number of sensors, minimal processing time and lower system and maintenance costs. The advancement in sensor and wireless communications technologies enables attaching a MEMS accelerometer with a miniaturised wireless data acquisition unit directly to the rotor without altering the machine dynamics. In this study, OSVM is analysed during constant speed and run-up operations of a test rig. The observations showed response modulation, hence, a Finite Element (FE) analysis has been carried out to help interpret the experimental observations. The FE analysis confirmed that the modulation is due to the rotary motion of the on-shaft sensor. A demodulation method has been developed to solve this problem. The FD capability of OSVM has been compared to that of OBVM using conventional analysis where the former provided more efficient diagnosis with less number of sensors. To incorporate more features, a method has been developed to diagnose faults based on Principal Component Analysis and Nearest Neighbour classifier. Furthermore, the method is enhanced using Linear Discriminant Analysis to do the diagnosis without the need for a classifier. Another faults diagnosis method has been developed that ensures the generalisation of extracted faults features from OSVM data of a specific machine to similar machines mounted on different foundations.

Submitted by Biblioteca Central (biblioteca@unicap.br) on 2018-06-05T17:18:37Z No. of bitstreams: 2 vicente_natanael_lima_silva.pdf: 2871330 bytes, checksum: 1730e0371d28b2975de3c999a484a82b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-06-05T17:18:37Z (GMT). No. of bitstreams: 2 vicente_natanael_lima_silva.pdf: 2871330 bytes, checksum: 1730e0371d28b2975de3c999a484a82b (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-04-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES# / #2075167498588264571# / #600 / In the State of Pernambuco, as well as throughout the Northeast region of Brazil, the expressive interaction between climate elements and human activities is evident. Numerous scientific studies have already demonstrated a significant correlation between climate behavior with social, economic, cultural, etc. This work served as a case study of the application of the multivariate statistical technique of Principal Components Analysis (PCA) in the making of socioeconomic diagnoses, where the elements of the climate were used as independent variables on the socioeconomic responses (Gross Domestic Product and Municipal Development Index) Of some municipalities that presented significant development in the State of Pernambuco - Brazil, between 1999 and 2013. Even considering the climatic, socioeconomic and essential dependence of water for the economic development of the municipalities studied, the PCA showed that the socioeconomic indexes of the municipalities located in the Sertão (Petrolina and Arcoverde) will present a higher correlation with the indices of temperature and Insulation, in the Agreste and Zona da Mata (Garanhuns and Surubim) evaporation and temperatures, in the Litoral (Recife) precipitation and humidity. The PCA was also effective in allowing the removal or disposal of variables that presented low variability or were redundant because they were correlated with those of greater importance for the first two main components. Understanding the behavior of climate elements and their consequences on human activities is of fundamental importance in helping public policies to mitigate the adverse effects of environmental change. / No Estado de Pernambuco, assim como em toda a região do Nordeste do Brasil, é evidente a expressiva interação existente entre os elementos do clima e as atividades humanas. Inúmeros estudos científicos já demostraram uma significativa correlação entre o comportamento climático com os aspectos sociais, econômicos, culturais, etc. Este trabalho serviu como estudo de caso da aplicação da técnica estatística multivariada de Análise de Componentes Principais (ACP) na confecção de diagnósticos socioeconômicos, onde foram utilizados os elementos do clima como independentes sobre as variáveis respostas socioeconômicas (Produto Interno Bruto e Índice de Desenvolvimento Municipal) de alguns municípios que apresentaram expressivo desenvolvimento no Estado de Pernambuco – Brasil, entre os anos de 1999 e 2013. Mesmo considerando as diferenças climáticas, socioeconômicas e a imprescindível dependência da água para o desenvolvimento econômico dos municípios estudados, a ACP demostrou que os índices socioeconômicos dos municípios localizados no Sertão (Petrolina e Arcoverde) apresentarão maior correlação com os índices de temperaturas e Insolação, no Agreste e Zona da Mata (Garanhuns e Surubim) a evaporação e temperaturas, no Litoral (Recife) a precipitação e umidade. ACP mostrou-se também efetiva em permitir a retirada ou descarte de variáveis que apresentaram baixa variabilidade ou foram redundantes por estarem correlacionadas com as de maior importância para dois primeiros componentes principais. A compreensão do comportamento dos elementos do clima e de suas consequências sobre as atividades humanas é de fundamental importância no auxílio às políticas públicas, que visem à mitigação de efeitos adversos provocados pelas alterações ambientais.

S rychlým rozvojem technologie multispektrálního zobrazování v posledních desetiletích obrázky získané zobrazovacími systémy obsahují nejen barevná pásma RGB v každodenním životě, ale také mají multispektrální barevná pásma a vysoké prostorové rozlišení v multispektrálních obrazových datech. Díky tomu obrázky obsahují bohaté informace o charakteristických cílových oblastech. Fúze obrazu je také důležitou větví v oblasti zpracování obrazu, kde je více obrázků ze stejné oblasti ve stejné výšce sloučeno do jednoho obrazu. Poté se zlepší korelace mezi spektrálními informacemi multispektrálních obrazů. Aby se informace na obrázku neztratily. Tato práce obsahuje popis návrhu a implementace multispektrálního obrazového systému, předzpracování multispektrálních obrazů, fúzi multispektrálních obrazů a analýzu hlavních komponent. Nakonec je představeno hodnocení celého systému.

This diploma thesis deals with using laser-induced breakdown plasma spectroscopy for determining the elemental structure of unknown samples. It was necessary to design an appropriate method to qualify material by laser-induced emission spectrum. Pretreatment of data and using a variety of chemometrics methods had to be done in order to qualify the structure of elements. We achieved a required solution by projecting the data to a new PCA space, creating clusters and computing the Euclidean distance between each cluster. The experiment in the practical part was set to detect an interface of two elements. We created a data file simulating the ablation on the interface. This data set was gradually processed applying a mathematical-chemical-physical view. Several data procedures have been compiled: approximation by Lorenz, Gauss and Voigt function and also a pretreatment method such as the detection of outliers, standardization by several procedures and subsequent use of principal components analysis. A summarization of processes for input data is fully described in the thesis.

This thesis deals with the design and implementation of a tool for generating drums triggers from a downmix record. The work describes the preprocessing of the input audio signal and methods for the classification of strokes. The drum classification is based on the similarity of the signals in the frequency domain. Principal component analysis (PCA) was used to reduce the number of dimensions and to find the characteristic properties of the input data. The method support vector machine (SVM) was used to classify the data into individual classes representing parts of the drum kit. The software was programmed in Matlab. The classification model was trained on a set of 728 drum samples for seven categories (kick, snare, hi-hat, crash, ride, kick + hi-hat, snare + hi-hat). The success of the system in the classification is 75 %.

Background: A problematic area in today’s large scale distributed systems is the exponential amount of growing log data. Finding anomalies by observing and monitoring this data with manual human inspection methods becomes progressively more challenging, complex and time consuming. This is vital for making these systems available around-the-clock. Aim: The main objective of this study is to determine which are the most suitable Machine Learning (ML) algorithms and if they can live up to needs and requirements regarding optimization and efficiency in the log data monitoring area. Including what specific steps of the overall problem can be improved by using these algorithms for anomaly detection and classification on different real provided data logs. Approach: Initial pre-study is conducted, logs are collected and then preprocessed with log parsing tool Drain and regular expressions. The approach consisted of a combination of K-Means + XGBoost and respectively Principal Component Analysis (PCA) + K-Means + XGBoost. These was trained, tested and with different metrics individually evaluated against two datasets, one being a Server data log and on a HTTP Access log. Results: The results showed that both approaches performed very well on both datasets. Able to with high accuracy, precision and low calculation time classify, detect and make predictions on log data events. It was further shown that when applied without dimensionality reduction, PCA, results of the prediction model is slightly better, by a few percent. As for the prediction time, there was marginally small to no difference for when comparing the prediction time with and without PCA. Conclusions: Overall there are very small differences when comparing the results for with and without PCA. But in essence, it is better to do not use PCA and instead apply the original data on the ML models. The models performance is generally very dependent on the data being applied, it the initial preprocessing steps, size and it is structure, especially affecting the calculation time the most.